main_endpoints.py

# This file contains the endpoint implementations
# In production, merge this with main.py

@app.post("/mask-fill")
async def mask_fill(request: MaskFillRequest):
    """Fill [MASK] tokens in French legal text"""
    try:
        tokenizer = tokenizers['juribert_base']
        model = models['juribert_mlm']
        
        # Create pipeline
        fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
        
        # Get predictions
        predictions = fill_mask(request.text, top_k=request.top_k)
        
        return {
            "input": request.text,
            "predictions": [
                {
                    "sequence": pred['sequence'],
                    "score": pred['score'],
                    "token": pred['token_str']
                }
                for pred in predictions
            ]
        }
        
    except Exception as e:
        logger.error(f"Mask fill error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/embeddings")
async def generate_embeddings(request: EmbeddingRequest):
    """Generate embeddings for French legal texts"""
    try:
        tokenizer = tokenizers['juribert_base']
        model = models['juribert_base']
        
        embeddings = []
        for text in request.texts:
            # Tokenize
            inputs = tokenizer(
                text, 
                return_tensors="pt", 
                truncation=True, 
                max_length=512,
                padding=True
            )
            
            # Generate embeddings
            with torch.no_grad():
                outputs = model(**inputs)
                # Use CLS token embedding
                embedding = outputs.last_hidden_state[:, 0, :].squeeze().numpy()
                embeddings.append(embedding.tolist())
        
        return {
            "embeddings": embeddings,
            "dimension": len(embeddings[0]) if embeddings else 0
        }
        
    except Exception as e:
        logger.error(f"Embedding error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/ner")
async def extract_entities(request: NERRequest):
    """Extract named entities from French legal text"""
    try:
        # Use CamemBERT NER model
        ner_pipeline = models['camembert_ner']
        entities = ner_pipeline(request.text)
        
        # Format results
        formatted_entities = []
        for entity in entities:
            formatted_entities.append({
                "text": entity['word'],
                "type": entity['entity_group'],
                "score": entity['score'],
                "start": entity['start'],
                "end": entity['end']
            })
        
        return {
            "entities": formatted_entities,
            "text": request.text
        }
        
    except Exception as e:
        logger.error(f"NER error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/qa")
async def question_answering(request: QARequest):
    """Answer questions about French legal documents"""
    try:
        # Simple implementation for now
        # In production, use a fine-tuned QA model
        
        return {
            "question": request.question,
            "answer": "This feature requires a fine-tuned QA model. Please check back later.",
            "confidence": 0.0,
            "relevant_articles": [],
            "explanation": "QA model is being fine-tuned on French legal data"
        }
        
    except Exception as e:
        logger.error(f"QA error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/classify")
async def classify_document(request: ClassificationRequest):
    """Classify French legal documents"""
    try:
        # Simple keyword-based classification for now
        text_lower = request.text.lower()
        
        categories = {
            "contract": ["contrat", "accord", "convention", "parties"],
            "litigation": ["tribunal", "jugement", "litige", "procès"],
            "corporate": ["société", "sarl", "sas", "entreprise"],
            "employment": ["travail", "salarié", "employeur", "licenciement"]
        }
        
        scores = {}
        for category, keywords in categories.items():
            score = sum(1 for kw in keywords if kw in text_lower)
            if score > 0:
                scores[category] = score
        
        if not scores:
            primary_category = "general"
        else:
            primary_category = max(scores, key=scores.get)
        
        return {
            "primary_category": primary_category,
            "categories": [{"category": cat, "score": score} for cat, score in scores.items()],
            "confidence": 0.8 if scores else 0.5,
            "document_type": "legal_document",
            "legal_domain": primary_category
        }
        
    except Exception as e:
        logger.error(f"Classification error: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {
        "status": "healthy",
        "models_loaded": list(models.keys()),
        "timestamp": datetime.utcnow().isoformat()
    }

# Add this to main.py when deploying